from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting(against_lib="sklearnex", config="config.yml")
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	NaN	7.192	0.0	-1	1	NaN	0.046	0.002	0.243	0.243	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	NaN	7.250	0.0	-1	5	NaN	0.048	0.001	0.231	0.231	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.001	NaN	6.850	0.0	1	100	NaN	0.046	0.001	0.254	0.254	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	NaN	6.917	0.0	-1	100	NaN	0.048	0.001	0.241	0.241	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	NaN	6.992	0.0	1	5	NaN	0.047	0.001	0.242	0.242	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	NaN	6.931	0.0	1	1	NaN	0.048	0.000	0.238	0.238	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	NaN	0.357	0.0	-1	1	NaN	0.009	0.000	0.496	0.496	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.336	0.0	-1	5	NaN	0.009	0.000	0.530	0.530	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.355	0.0	1	100	NaN	0.009	0.000	0.486	0.487	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.334	0.0	-1	100	NaN	0.009	0.000	0.508	0.508	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.352	0.0	1	5	NaN	0.008	0.000	0.549	0.550	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.320	0.0	1	1	NaN	0.009	0.000	0.572	0.572	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.888	0.094	NaN	0.000	0.002	-1	1	0.663	0.182	0.013	10.389	10.415	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.002	NaN	0.000	0.022	-1	1	1.000	0.008	0.000	2.909	2.910	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.782	0.048	NaN	0.000	0.003	-1	5	0.757	0.181	0.003	15.378	15.380	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.003	NaN	0.000	0.024	-1	5	1.000	0.008	0.000	3.059	3.060	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.107	0.028	NaN	0.000	0.002	1	100	0.882	0.232	0.004	9.077	9.079	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.000	NaN	0.000	0.021	1	100	1.000	0.008	0.000	2.618	2.619	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.857	0.037	NaN	0.000	0.003	-1	100	0.882	0.229	0.006	12.474	12.478	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.002	NaN	0.000	0.024	-1	100	1.000	0.008	0.001	2.870	2.885	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.121	0.049	NaN	0.000	0.002	1	5	0.757	0.188	0.003	11.279	11.280	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.000	NaN	0.000	0.021	1	5	1.000	0.008	0.000	2.628	2.629	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.156	0.013	NaN	0.001	0.001	1	1	0.663	0.187	0.005	6.178	6.181	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	NaN	0.000	0.019	1	1	1.000	0.008	0.000	2.308	2.308	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.722	0.026	NaN	0.000	0.002	-1	1	0.896	0.029	0.001	59.096	59.167	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.004	NaN	0.000	0.006	-1	1	1.000	0.001	0.000	8.614	8.669	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.633	0.032	NaN	0.000	0.003	-1	5	0.922	0.030	0.001	87.236	87.254	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.002	NaN	0.000	0.006	-1	5	1.000	0.001	0.000	8.152	8.178	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.999	0.038	NaN	0.000	0.002	1	100	0.929	0.067	0.002	29.841	29.860	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	100	1.000	0.001	0.000	3.741	3.759	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.639	0.038	NaN	0.000	0.003	-1	100	0.929	0.065	0.002	40.903	40.928	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.004	NaN	0.000	0.007	-1	100	1.000	0.001	0.000	9.234	9.342	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.007	0.029	NaN	0.000	0.002	1	5	0.922	0.028	0.002	70.443	70.542	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	5	1.000	0.001	0.000	4.211	4.259	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.036	0.007	NaN	0.000	0.001	1	1	0.896	0.030	0.001	34.502	34.507	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	NaN	0.000	0.002	1	1	1.000	0.001	0.000	2.343	2.348	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.885	0.091	NaN	0.028	0.0	-1	1	NaN	0.747	0.030	3.864	3.867	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.484	0.055	NaN	0.023	0.0	-1	5	NaN	0.742	0.012	4.692	4.693	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.486	0.043	NaN	0.023	0.0	1	100	NaN	0.696	0.012	5.011	5.012	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.515	0.098	NaN	0.023	0.0	-1	100	NaN	0.712	0.019	4.937	4.938	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.447	0.071	NaN	0.023	0.0	1	5	NaN	0.695	0.017	4.961	4.963	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.508	0.065	NaN	0.023	0.0	1	1	NaN	0.710	0.008	4.940	4.940	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	NaN	0.019	0.0	-1	1	NaN	0.004	0.003	0.200	0.254	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.030	0.0	-1	5	NaN	0.002	0.002	0.218	0.278	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.028	0.0	1	100	NaN	0.001	0.000	0.542	0.596	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.031	0.0	-1	100	NaN	0.001	0.000	0.541	0.561	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.029	0.0	1	5	NaN	0.001	0.000	0.647	0.651	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.031	0.0	1	1	NaN	0.001	0.000	0.580	0.581	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.826	1.023	NaN	0.000	0.001	-1	1	0.929	0.105	0.003	7.852	7.856	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	1	1.000	0.000	0.000	9.380	9.610	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.000	0.348	NaN	0.000	0.001	-1	5	0.946	0.185	0.004	5.416	5.417	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	5	1.000	0.000	0.000	8.083	8.444	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.175	0.523	NaN	0.000	0.005	1	100	0.951	0.558	0.019	9.274	9.279	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	1	100	1.000	0.001	0.000	4.152	4.292	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2.791	0.325	NaN	0.000	0.003	-1	100	0.951	0.536	0.009	5.204	5.204	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	NaN	0.000	0.005	-1	100	1.000	0.001	0.000	6.747	6.995	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.601	0.394	NaN	0.000	0.002	1	5	0.946	0.184	0.002	8.689	8.689	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	NaN	0.000	0.002	1	5	1.000	0.000	0.000	3.742	3.861	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.909	0.364	NaN	0.000	0.001	1	1	0.929	0.098	0.002	9.299	9.300	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	3.537	3.691	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.026	0.016	NaN	0.001	0.000	-1	1	0.891	0.000	0.000	60.602	62.024	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	1	1.000	0.000	0.000	22.506	22.941	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.024	0.002	NaN	0.001	0.000	-1	5	0.911	0.001	0.000	35.781	36.036	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.001	NaN	0.000	0.002	-1	5	1.000	0.000	0.000	23.445	24.096	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.036	0.009	NaN	0.000	0.000	1	100	0.894	0.005	0.000	7.692	7.708	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	100	1.000	0.000	0.000	5.461	5.746	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.041	0.005	NaN	0.000	0.000	-1	100	0.894	0.006	0.002	7.147	7.491	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	100	1.000	0.000	0.000	21.833	22.558	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.019	0.001	NaN	0.001	0.000	1	5	0.911	0.001	0.000	27.058	27.092	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	5	1.000	0.000	0.000	5.152	5.342	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.018	0.001	NaN	0.001	0.000	1	1	0.891	0.001	0.000	34.567	36.195	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	5.285	5.542	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.570	0.174	30	0.028	0.0	random	NaN	0.405	0.031	1.406	1.411	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.608	0.098	30	0.026	0.0	k-means++	NaN	0.436	0.037	1.394	1.399	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.659	0.218	30	0.141	0.0	random	NaN	2.686	0.035	2.107	2.107	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.818	0.062	30	0.138	0.0	k-means++	NaN	2.842	0.021	2.047	2.047	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.009	0.000	random	0.001	0.0	0.0	12.101	13.487	See	See
2	KMeans_tall	predict	1000000	1	2	0.003	0.002	30	0.000	0.003	random	1.000	0.0	0.0	16.535	26.665	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.010	0.000	k-means++	0.001	0.0	0.0	12.612	14.099	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	1.000	0.0	0.0	12.728	13.209	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.422	0.000	random	0.002	0.0	0.0	6.819	7.156	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	random	1.000	0.0	0.0	14.578	15.220	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.394	0.000	k-means++	0.002	0.0	0.0	7.675	7.969	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	1.000	0.0	0.0	14.257	14.541	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.082	0.003	20	0.002	0.0	random	NaN	0.028	0.001	2.921	2.924	See	See
3	KMeans_short	fit	10000	10000	2	0.229	0.007	20	0.001	0.0	k-means++	NaN	0.097	0.018	2.356	2.395	See	See
6	KMeans_short	fit	10000	10000	100	0.209	0.005	20	0.038	0.0	random	NaN	0.113	0.004	1.850	1.851	See	See
9	KMeans_short	fit	10000	10000	100	0.629	0.012	20	0.013	0.0	k-means++	NaN	0.316	0.007	1.991	1.991	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	random	0.000	0.000	0.0	4.010	4.023	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	1.000	0.000	0.0	15.046	15.634	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	k-means++	0.001	0.000	0.0	4.143	4.156	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	k-means++	1.000	0.000	0.0	12.900	13.229	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.303	0.000	random	0.279	0.001	0.0	2.680	2.696	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.001	0.002	random	1.000	0.000	0.0	9.524	9.746	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.293	0.000	k-means++	0.317	0.001	0.0	2.785	2.812	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.001	0.002	k-means++	1.000	0.000	0.0	10.485	10.568	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	10.768	0.361	[20]	0.074	0.000	NaN	NaN	NaN	NaN	NaN	1.869	0.051	5.763	5.765	See	See
3	LogisticRegression	fit	1000	1000	10000	0.963	0.762	[26]	0.083	0.001	NaN	NaN	NaN	NaN	NaN	0.767	0.018	1.256	1.256	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.001	[20]	1.676	0.0	NaN	NaN	NaN	NaN	0.56	0.001	0.001	0.600	1.237	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.000	[20]	0.014	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.000	0.365	0.370	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.000	[26]	4.860	0.0	NaN	NaN	NaN	NaN	0.35	0.003	0.000	0.516	0.518	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.000	[26]	1.006	0.0	NaN	NaN	NaN	NaN	0.00	0.001	0.000	0.116	0.116	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.184	0.003	NaN	0.435	0.0	NaN	NaN	NaN	0.185	0.003	0.994	0.995	See	See
3	Ridge	fit	1000000	1000000	100	1.152	0.091	NaN	0.694	0.0	NaN	NaN	NaN	0.305	0.240	3.780	4.809	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.01	0.0	NaN	8.037	0.0	NaN	NaN	0.083	0.016	0.0	0.605	0.605	See	See
2	Ridge	predict	1000	1	10000	0.00	0.0	NaN	1.189	0.0	NaN	NaN	NaN	0.000	0.0	0.704	0.758	See	See
4	Ridge	predict	1000000	1000	100	0.00	0.0	NaN	6.091	0.0	NaN	NaN	1.000	0.000	0.0	0.422	0.622	See	See
5	Ridge	predict	1000000	1	100	0.00	0.0	NaN	0.016	0.0	NaN	NaN	NaN	0.000	0.0	0.581	0.610	See	See